A window size of 5 seconds and response size of 20 seconds would satisfy
the set conditions, but even though the distillation column is very
responsive, 5 seconds might be too short to capture the effects of
changes in control variables. Hence, a window size of 10 s and a
response size of 20 s are chosen for the further training and
optimization of models.
The following ML methods are compared for the pressure drop forecast:
linear regression, random forest, extra trees, AdaBoost, and gradient
boosting regression. To achieve the best performance, the number of
estimators (decision trees) and the depth of the decision trees were
optimized for the bagging and boosting regressors in a k-fold
cross-validated grid search (k = 5) utilizing the training data.
Finally, the performance of every model is compared in Table 1 based on
the test data set and the chosen metrics. Note that AdaBoost and the
extra trees regressor are combined via a voting regressor for an
additional model as their training time is quite low. Due to the
different working principles, weaknesses of the respective models could
be eliminated by combining them. The training time is based on an INTEL
Core i5-6600K CPU overclocked to 4.5 GHz.
Table 4: Accuracy of
pressure drop forecast for different algorithm methods.